PMID- 27618901
OWN - NLM
STAT- MEDLINE
DCOM- 20180205
LR  - 20181113
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 16
IP  - 9
DP  - 2016 Aug 27
TI  - Identifying Plant Part Composition of Forest Logging Residue Using Infrared 
      Spectral Data and Linear Discriminant Analysis.
LID - 10.3390/s16091375 [doi]
LID - 1375
AB  - As new markets, technologies and economies evolve in the low carbon bioeconomy, 
      forest logging residue, a largely untapped renewable resource will play a vital 
      role. The feedstock can however be variable depending on plant species and plant 
      part component. This heterogeneity can influence the physical, chemical and 
      thermochemical properties of the material, and thus the final yield and quality 
      of products. Although it is challenging to control compositional variability of a 
      batch of feedstock, it is feasible to monitor this heterogeneity and make the 
      necessary changes in process parameters. Such a system will be a first step 
      towards optimization, quality assurance and cost-effectiveness of processes in 
      the emerging biofuel/chemical industry. The objective of this study was therefore 
      to qualitatively classify forest logging residue made up of different plant parts 
      using both near infrared spectroscopy (NIRS) and Fourier transform infrared 
      spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest 
      logging residue harvested from several Pinus taeda (loblolly pine) plantations in 
      Alabama, USA, were classified into three plant part components: clean wood, wood 
      and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear 
      discriminant functions had classification accuracies of over 96% for both NIRS 
      and FTIRS based models. An extra factor/principal component (PC) was however 
      needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR 
      and FTIR spectra showed that, the statistically different amount of cellulose in 
      the three plant part components of logging residue contributed to their initial 
      separation. This study demonstrated that NIR or FTIR spectroscopy coupled with 
      PCA and LDA has the potential to be used as a high throughput tool in classifying 
      the plant part makeup of a batch of forest logging residue feedstock. Thus, 
      NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of 
      forest biomass so that the appropriate online adjustments to parameters can be 
      made in time to ensure process optimization and product quality.
FAU - Acquah, Gifty E
AU  - Acquah GE
AD  - Forest Products Development Center, School of Forestry and Wildlife Sciences, 
      Auburn University, 520 Devall Drive, Auburn, AL 36849, USA. gea0002@auburn.edu.
FAU - Via, Brian K
AU  - Via BK
AD  - Forest Products Development Center, School of Forestry and Wildlife Sciences, 
      Auburn University, 520 Devall Drive, Auburn, AL 36849, USA. bkv0003@auburn.edu.
FAU - Billor, Nedret
AU  - Billor N
AD  - Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849, 
      USA. billone@auburn.edu.
FAU - Fasina, Oladiran O
AU  - Fasina OO
AD  - Center for Bioenergy and Bioproducts, Department of Biosystems Engineering, 
      Auburn University, 350 Mell Street, Auburn, AL 36849, USA. fasinoo@auburn.edu.
FAU - Eckhardt, Lori G
AU  - Eckhardt LG
AD  - Forest Health Dynamics Laboratory, School of Forestry and Wildlife Sciences, 
      Auburn University, 602 Duncan Drive, Auburn, AL 36849, USA. eckhalg@auburn.edu.
LA  - eng
PT  - Journal Article
DEP - 20160827
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Discriminant Analysis
MH  - *Forests
MH  - Plants/*anatomy & histology
MH  - Principal Component Analysis
MH  - Reproducibility of Results
MH  - Spectroscopy, Fourier Transform Infrared
MH  - Spectroscopy, Near-Infrared
PMC - PMC5038653
OTO - NOTNLM
OT  - bioeconomy
OT  - forest biomass
OT  - fourier transform infrared spectroscopy
OT  - linear discriminant analysis
OT  - near infrared spectroscopy
OT  - principal component analysis
OT  - process optimization
COIS- The authors declare no conflict of interest.
EDAT- 2016/09/14 06:00
MHDA- 2018/02/06 06:00
PMCR- 2016/09/01
CRDT- 2016/09/14 06:00
PHST- 2016/05/21 00:00 [received]
PHST- 2016/08/22 00:00 [revised]
PHST- 2016/08/23 00:00 [accepted]
PHST- 2016/09/14 06:00 [entrez]
PHST- 2016/09/14 06:00 [pubmed]
PHST- 2018/02/06 06:00 [medline]
PHST- 2016/09/01 00:00 [pmc-release]
AID - s16091375 [pii]
AID - sensors-16-01375 [pii]
AID - 10.3390/s16091375 [doi]
PST - epublish
SO  - Sensors (Basel). 2016 Aug 27;16(9):1375. doi: 10.3390/s16091375.